Overview

Dataset statistics

Number of variables19
Number of observations74690
Missing cells288355
Missing cells (%)20.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 MiB
Average record size in memory152.0 B

Variable types

CAT10
NUM8
UNSUPPORTED1

Warnings

UploadDate has constant value "74690" Constant
Common Name has a high cardinality: 570 distinct values High cardinality
Scientific Name has a high cardinality: 535 distinct values High cardinality
Genus has a high cardinality: 168 distinct values High cardinality
Family has a high cardinality: 60 distinct values High cardinality
Date Planted has a high cardinality: 1779 distinct values High cardinality
CoordinateLocation has a high cardinality: 74671 distinct values High cardinality
Easting is highly correlated with LongitudeHigh correlation
Longitude is highly correlated with EastingHigh correlation
Northing is highly correlated with LatitudeHigh correlation
Latitude is highly correlated with NorthingHigh correlation
Diameter Breast Height has 49268 (66.0%) missing values Missing
Year Planted has 8193 (11.0%) missing values Missing
Date Planted has 8193 (11.0%) missing values Missing
Age Description has 49286 (66.0%) missing values Missing
Useful Life Expectency has 49286 (66.0%) missing values Missing
Useful Life Expectency Value has 49286 (66.0%) missing values Missing
Precinct has 74690 (100.0%) missing values Missing
CoordinateLocation is uniformly distributed Uniform
CoM ID has unique values Unique
Precinct is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2020-10-02 12:43:57.739391
Analysis finished2020-10-02 12:44:13.838570
Duration16.1 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CoM ID
Real number (ℝ≥0)

UNIQUE

Distinct74690
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1340215.773
Minimum1013381
Maximum1787466
Zeros0
Zeros (%)0.0%
Memory size583.5 KiB
2020-10-02T22:44:13.993722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1013381
5-th percentile1021462.45
Q11047582.25
median1359858.5
Q31578851.75
95-th percentile1769320.65
Maximum1787466
Range774085
Interquartile range (IQR)531269.5

Descriptive statistics

Standard deviation282800.3705
Coefficient of variation (CV)0.2110110747
Kurtosis-1.621929265
Mean1340215.773
Median Absolute Deviation (MAD)303008
Skewness0.1139895432
Sum1.00100716e+11
Variance7.997604955e+10
MonotocityNot monotonic
2020-10-02T22:44:14.186579image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10526701< 0.1%
 
10512841< 0.1%
 
15182561< 0.1%
 
15223541< 0.1%
 
17824511< 0.1%
 
15100681< 0.1%
 
15121191< 0.1%
 
15244131< 0.1%
 
17346771< 0.1%
 
17827001< 0.1%
 
Other values (74680)74680> 99.9%
 
ValueCountFrequency (%) 
10133811< 0.1%
 
10133821< 0.1%
 
10133831< 0.1%
 
10133841< 0.1%
 
10133851< 0.1%
 
ValueCountFrequency (%) 
17874661< 0.1%
 
17874651< 0.1%
 
17874641< 0.1%
 
17874631< 0.1%
 
17874621< 0.1%
 

Common Name
Categorical

HIGH CARDINALITY

Distinct570
Distinct (%)0.8%
Missing10
Missing (%)< 0.1%
Memory size583.5 KiB
River red gum
7969 
London Plane
 
5078
Drooping sheoak
 
3078
Spotted Gum
 
3011
Yellow Box
 
2921
Other values (565)
52623 
ValueCountFrequency (%) 
River red gum796910.7%
 
London Plane50786.8%
 
Drooping sheoak30784.1%
 
Spotted Gum30114.0%
 
Yellow Box29213.9%
 
Black Wattle26223.5%
 
English Elm22123.0%
 
Elm17372.3%
 
Lemon Scented Gum14381.9%
 
Yellow Gum14291.9%
 
Other values (560)4318557.8%
 
2020-10-02T22:44:14.397923image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique84 ?
Unique (%)0.1%
2020-10-02T22:44:14.582693image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length12
Mean length12.70767171
Min length3

Scientific Name
Categorical

HIGH CARDINALITY

Distinct535
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Memory size583.5 KiB
Eucalyptus camaldulensis
7969 
Platanus x acerifolia
 
5146
Allocasuarina verticillata
 
3078
Corymbia maculata
 
3011
Eucalyptus melliodora
 
2921
Other values (530)
52564 
ValueCountFrequency (%) 
Eucalyptus camaldulensis796910.7%
 
Platanus x acerifolia51466.9%
 
Allocasuarina verticillata30784.1%
 
Corymbia maculata30114.0%
 
Eucalyptus melliodora29213.9%
 
Ulmus procera22123.0%
 
Ulmus unknown17372.3%
 
Eucalyptus leucoxylon15692.1%
 
Corymbia citriodora14792.0%
 
Acacia mearnsii14511.9%
 
Other values (525)4411659.1%
 
2020-10-02T22:44:14.771276image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique90 ?
Unique (%)0.1%
2020-10-02T22:44:14.965199image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length41
Median length19
Mean length19.17338332
Min length3

Genus
Categorical

HIGH CARDINALITY

Distinct168
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size583.5 KiB
Eucalyptus
17211 
Acacia
6382 
Ulmus
5921 
Platanus
5695 
Corymbia
4792 
Other values (163)
34688 
ValueCountFrequency (%) 
Eucalyptus1721123.0%
 
Acacia63828.5%
 
Ulmus59217.9%
 
Platanus56957.6%
 
Corymbia47926.4%
 
Allocasuarina35104.7%
 
Quercus23223.1%
 
Casuarina18642.5%
 
Ficus16222.2%
 
Melaleuca15162.0%
 
Other values (158)2385431.9%
 
2020-10-02T22:44:15.165269image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique24 ?
Unique (%)< 0.1%
2020-10-02T22:44:15.535662image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length8
Mean length8.270384255
Min length3

Family
Categorical

HIGH CARDINALITY

Distinct60
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size583.5 KiB
Myrtaceae
28470 
Fabaceae
6917 
Ulmaceae
6644 
Platanaceae
5695 
Casuarinaceae
5374 
Other values (55)
21589 
ValueCountFrequency (%) 
Myrtaceae2847038.1%
 
Fabaceae69179.3%
 
Ulmaceae66448.9%
 
Platanaceae56957.6%
 
Casuarinaceae53747.2%
 
Fagaceae23303.1%
 
Proteaceae19962.7%
 
Sapindaceae17542.3%
 
Moraceae16352.2%
 
Pittosporaceae13811.8%
 
Other values (50)1249316.7%
 
2020-10-02T22:44:15.721532image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7 ?
Unique (%)< 0.1%
2020-10-02T22:44:15.897927image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length9
Mean length9.584616415
Min length3

Diameter Breast Height
Real number (ℝ≥0)

MISSING

Distinct220
Distinct (%)0.9%
Missing49268
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean37.59649123
Minimum0
Maximum347
Zeros255
Zeros (%)0.3%
Memory size583.5 KiB
2020-10-02T22:44:16.062188image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q114
median29
Q356
95-th percentile97
Maximum347
Range347
Interquartile range (IQR)42

Descriptive statistics

Standard deviation31.55417029
Coefficient of variation (CV)0.8392849773
Kurtosis3.599336201
Mean37.59649123
Median Absolute Deviation (MAD)19
Skewness1.45523555
Sum955778
Variance995.6656625
MonotocityNot monotonic
2020-10-02T22:44:16.238731image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
46080.8%
 
56060.8%
 
65870.8%
 
35630.8%
 
75220.7%
 
155040.7%
 
194900.7%
 
174880.7%
 
104760.6%
 
204760.6%
 
Other values (210)2010226.9%
 
(Missing)4926866.0%
 
ValueCountFrequency (%) 
02550.3%
 
11390.2%
 
23590.5%
 
35630.8%
 
46080.8%
 
ValueCountFrequency (%) 
3471< 0.1%
 
3101< 0.1%
 
2831< 0.1%
 
2731< 0.1%
 
2681< 0.1%
 

Year Planted
Real number (ℝ≥0)

MISSING

Distinct24
Distinct (%)< 0.1%
Missing8193
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean1998.997489
Minimum1899
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size583.5 KiB
2020-10-02T22:44:16.401632image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1899
5-th percentile1900
Q11998
median2008
Q32015
95-th percentile2019
Maximum2020
Range121
Interquartile range (IQR)17

Descriptive statistics

Standard deviation30.4362635
Coefficient of variation (CV)0.01522576375
Kurtosis6.091514537
Mean1998.997489
Median Absolute Deviation (MAD)10
Skewness-2.699518887
Sum132927336
Variance926.366136
MonotocityNot monotonic
2020-10-02T22:44:16.547721image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
19981225216.4%
 
199766628.9%
 
190053237.1%
 
201938835.2%
 
201537535.0%
 
201732384.3%
 
201231474.2%
 
200630794.1%
 
199930414.1%
 
202028483.8%
 
Other values (14)1927125.8%
 
(Missing)819311.0%
 
ValueCountFrequency (%) 
1899480.1%
 
190053237.1%
 
19771< 0.1%
 
199766628.9%
 
19981225216.4%
 
ValueCountFrequency (%) 
202028483.8%
 
201938835.2%
 
201825843.5%
 
201732384.3%
 
201625023.3%
 

Date Planted
Categorical

HIGH CARDINALITY
MISSING

Distinct1779
Distinct (%)2.7%
Missing8193
Missing (%)11.0%
Memory size583.5 KiB
01/01/2000
 
3268
02/01/2000
 
3260
27/04/2012
 
999
04/07/2009
 
660
14/12/1998
 
582
Other values (1774)
57728 
ValueCountFrequency (%) 
01/01/200032684.4%
 
02/01/200032604.4%
 
27/04/20129991.3%
 
04/07/20096600.9%
 
14/12/19985820.8%
 
16/12/19985780.8%
 
17/12/19985400.7%
 
12/11/19984960.7%
 
12/03/19984900.7%
 
13/01/19994750.6%
 
Other values (1769)5514973.8%
 
(Missing)819311.0%
 
2020-10-02T22:44:16.738379image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique252 ?
Unique (%)0.4%
2020-10-02T22:44:16.912743image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.232146204
Min length3

Age Description
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing49286
Missing (%)66.0%
Memory size583.5 KiB
Semi-Mature
10632 
Mature
9025 
Juvenile
4027 
New
1423 
Over-mature
 
297
ValueCountFrequency (%) 
Semi-Mature1063214.2%
 
Mature902512.1%
 
Juvenile40275.4%
 
New14231.9%
 
Over-mature2970.4%
 
(Missing)4928666.0%
 
2020-10-02T22:44:17.060516image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T22:44:17.155616image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:17.284484image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length4.802677735
Min length3

Useful Life Expectency
Categorical

MISSING

Distinct10
Distinct (%)< 0.1%
Missing49286
Missing (%)66.0%
Memory size583.5 KiB
31-60 years
7363 
21-30 years
5004 
11-20 years
4311 
61+ years
3163 
6-10 years (>50% canopy)
1870 
Other values (5)
3693 
ValueCountFrequency (%) 
31-60 years73639.9%
 
21-30 years50046.7%
 
11-20 years43115.8%
 
61+ years31634.2%
 
6-10 years (>50% canopy)18702.5%
 
1-5 years (<50% canopy)15392.1%
 
6-10 years (<50% canopy)13751.8%
 
1-5 years (>50% canopy)3350.4%
 
<1 year3170.4%
 
1-5 years1270.2%
 
(Missing)4928666.0%
 
2020-10-02T22:44:17.440563image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T22:44:17.549904image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:17.712763image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length3
Mean length6.481818182
Min length3

Useful Life Expectency Value
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing49286
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean38.33774209
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Memory size583.5 KiB
2020-10-02T22:44:17.832714image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q120
median30
Q360
95-th percentile80
Maximum80
Range79
Interquartile range (IQR)40

Descriptive statistics

Standard deviation25.10811028
Coefficient of variation (CV)0.6549188582
Kurtosis-1.331661476
Mean38.33774209
Median Absolute Deviation (MAD)25
Skewness0.2726180147
Sum973932
Variance630.4172016
MonotocityNot monotonic
2020-10-02T22:44:17.957109image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6073639.9%
 
3050046.7%
 
2043115.8%
 
1032454.3%
 
8031634.2%
 
520012.7%
 
13170.4%
 
(Missing)4928666.0%
 
ValueCountFrequency (%) 
13170.4%
 
520012.7%
 
1032454.3%
 
2043115.8%
 
3050046.7%
 
ValueCountFrequency (%) 
8031634.2%
 
6073639.9%
 
3050046.7%
 
2043115.8%
 
1032454.3%
 

Precinct
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing74690
Missing (%)100.0%
Memory size583.6 KiB

Located in
Categorical

Distinct2
Distinct (%)< 0.1%
Missing140
Missing (%)0.2%
Memory size583.5 KiB
Park
44565 
Street
29985 
ValueCountFrequency (%) 
Park4456559.7%
 
Street2998540.1%
 
(Missing)1400.2%
 
2020-10-02T22:44:18.109333image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T22:44:18.201587image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:18.303261image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length4.801044317
Min length3

UploadDate
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size583.5 KiB
01/10/2020
74690 
ValueCountFrequency (%) 
01/10/202074690100.0%
 
2020-10-02T22:44:18.436676image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-02T22:44:18.519132image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:18.613729image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

CoordinateLocation
Categorical

HIGH CARDINALITY
UNIFORM

Distinct74671
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size583.5 KiB
(-37.7914833480749, 144.94707053642645)
 
2
(-37.78241357208308, 144.94903799941775)
 
2
(-37.78402113407502, 144.95636067748285)
 
2
(-37.78357828352511, 144.95565900304265)
 
2
(-37.78412681859444, 144.95657225912905)
 
2
Other values (74666)
74680 
ValueCountFrequency (%) 
(-37.7914833480749, 144.94707053642645)2< 0.1%
 
(-37.78241357208308, 144.94903799941775)2< 0.1%
 
(-37.78402113407502, 144.95636067748285)2< 0.1%
 
(-37.78357828352511, 144.95565900304265)2< 0.1%
 
(-37.78412681859444, 144.95657225912905)2< 0.1%
 
(-37.7840762536616, 144.95582911550218)2< 0.1%
 
(-37.785400121796584, 144.95432601989748)2< 0.1%
 
(-37.78344686646531, 144.95543950381864)2< 0.1%
 
(-37.783626795574406, 144.95572295612783)2< 0.1%
 
(-37.783743992045764, 144.9557514065785)2< 0.1%
 
Other values (74661)74670> 99.9%
 
2020-10-02T22:44:18.983977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique74652 ?
Unique (%)99.9%
2020-10-02T22:44:19.158438image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length41
Median length40
Mean length39.89732227
Min length35

Latitude
Real number (ℝ)

HIGH CORRELATION

Distinct74671
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.80134838
Minimum-37.85053172
Maximum-37.77551137
Zeros0
Zeros (%)0.0%
Memory size583.5 KiB
2020-10-02T22:44:19.357059image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-37.85053172
5-th percentile-37.83022459
Q1-37.81409888
median-37.79674565
Q3-37.78883641
95-th percentile-37.78012744
Maximum-37.77551137
Range0.07502034875
Interquartile range (IQR)0.02526246243

Descriptive statistics

Standard deviation0.01633835597
Coefficient of variation (CV)-0.0004322162218
Kurtosis-0.5294818137
Mean-37.80134838
Median Absolute Deviation (MAD)0.01092711602
Skewness-0.6245853954
Sum-2823382.711
Variance0.000266941876
MonotocityNot monotonic
2020-10-02T22:44:19.531951image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-37.782413572< 0.1%
 
-37.78362682< 0.1%
 
-37.783402982< 0.1%
 
-37.783270112< 0.1%
 
-37.784021132< 0.1%
 
-37.78413182< 0.1%
 
-37.78685152< 0.1%
 
-37.783446872< 0.1%
 
-37.783743992< 0.1%
 
-37.783262892< 0.1%
 
Other values (74661)74670> 99.9%
 
ValueCountFrequency (%) 
-37.850531721< 0.1%
 
-37.850518191< 0.1%
 
-37.850499151< 0.1%
 
-37.850485871< 0.1%
 
-37.85048451< 0.1%
 
ValueCountFrequency (%) 
-37.775511371< 0.1%
 
-37.775518391< 0.1%
 
-37.775523511< 0.1%
 
-37.775534461< 0.1%
 
-37.775541121< 0.1%
 

Longitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct74671
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.9516034
Minimum144.9004497
Maximum144.9910558
Zeros0
Zeros (%)0.0%
Memory size583.5 KiB
2020-10-02T22:44:19.751432image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum144.9004497
5-th percentile144.9164628
Q1144.94286
median144.9514732
Q3144.9638143
95-th percentile144.982993
Maximum144.9910558
Range0.0906060254
Interquartile range (IQR)0.02095434501

Descriptive statistics

Standard deviation0.0188309371
Coefficient of variation (CV)0.0001299118924
Kurtosis-0.2996784642
Mean144.9516034
Median Absolute Deviation (MAD)0.009768643564
Skewness-0.1872139784
Sum10826435.25
Variance0.0003546041921
MonotocityNot monotonic
2020-10-02T22:44:19.924204image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
144.9543262< 0.1%
 
144.95545522< 0.1%
 
144.96293812< 0.1%
 
144.95582912< 0.1%
 
144.9556592< 0.1%
 
144.9557232< 0.1%
 
144.95657232< 0.1%
 
144.95533342< 0.1%
 
144.95504772< 0.1%
 
144.95613772< 0.1%
 
Other values (74661)74670> 99.9%
 
ValueCountFrequency (%) 
144.90044971< 0.1%
 
144.90047211< 0.1%
 
144.90047681< 0.1%
 
144.90049491< 0.1%
 
144.90052481< 0.1%
 
ValueCountFrequency (%) 
144.99105581< 0.1%
 
144.991041< 0.1%
 
144.99103911< 0.1%
 
144.99102141< 0.1%
 
144.9910111< 0.1%
 

Easting
Real number (ℝ≥0)

HIGH CORRELATION

Distinct68570
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319662.4184
Minimum315230.55
Maximum323159.33
Zeros0
Zeros (%)0.0%
Memory size583.5 KiB
2020-10-02T22:44:20.129304image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum315230.55
5-th percentile316556.1465
Q1318879.015
median319627.51
Q3320753.6725
95-th percentile322495.971
Maximum323159.33
Range7928.78
Interquartile range (IQR)1874.6575

Descriptive statistics

Standard deviation1673.397429
Coefficient of variation (CV)0.005234889473
Kurtosis-0.3263315695
Mean319662.4184
Median Absolute Deviation (MAD)865.03
Skewness-0.1402760251
Sum2.387558603e+10
Variance2800258.956
MonotocityNot monotonic
2020-10-02T22:44:20.302456image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
319033.45< 0.1%
 
319522.35< 0.1%
 
319719.75< 0.1%
 
319724.455< 0.1%
 
319365.044< 0.1%
 
319451.964< 0.1%
 
320339.814< 0.1%
 
319626.54< 0.1%
 
320339.094< 0.1%
 
319195.794< 0.1%
 
Other values (68560)7464699.9%
 
ValueCountFrequency (%) 
315230.551< 0.1%
 
315232.111< 0.1%
 
315232.431< 0.1%
 
315234.541< 0.1%
 
315236.941< 0.1%
 
ValueCountFrequency (%) 
323159.331< 0.1%
 
323157.941< 0.1%
 
323157.891< 0.1%
 
323156.691< 0.1%
 
323156.321< 0.1%
 

Northing
Real number (ℝ≥0)

HIGH CORRELATION

Distinct68878
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5814248.917
Minimum5808853.08
Maximum5817092.14
Zeros0
Zeros (%)0.0%
Memory size583.5 KiB
2020-10-02T22:44:20.518309image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum5808853.08
5-th percentile5811062.2
Q15812868.442
median5814736.7
Q35815629.835
95-th percentile5816604.429
Maximum5817092.14
Range8239.06
Interquartile range (IQR)2761.3925

Descriptive statistics

Standard deviation1799.528103
Coefficient of variation (CV)0.0003095031066
Kurtosis-0.5455257753
Mean5814248.917
Median Absolute Deviation (MAD)1215.125
Skewness-0.60924807
Sum4.342662516e+11
Variance3238301.392
MonotocityNot monotonic
2020-10-02T22:44:20.884838image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5814938.176< 0.1%
 
5814937.895< 0.1%
 
5815513.555< 0.1%
 
5815525.175< 0.1%
 
5814936.954< 0.1%
 
5816395.064< 0.1%
 
5815529.744< 0.1%
 
5816440.764< 0.1%
 
5815460.994< 0.1%
 
5814075.614< 0.1%
 
Other values (68868)7464599.9%
 
ValueCountFrequency (%) 
5808853.081< 0.1%
 
5808854.381< 0.1%
 
5808856.251< 0.1%
 
5808857.561< 0.1%
 
5808857.591< 0.1%
 
ValueCountFrequency (%) 
5817092.141< 0.1%
 
5817091.231< 0.1%
 
5817090.921< 0.1%
 
5817089.831< 0.1%
 
5817089.231< 0.1%
 

Interactions

2020-10-02T22:44:03.596790image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:03.722271image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:03.839301image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:03.949267image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:04.067535image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:04.186544image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:04.297789image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:04.417911image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:04.538476image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:04.658442image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:04.785180image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:04.905480image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:05.034709image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-02T22:44:05.890288image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:06.010002image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-02T22:44:06.253219image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-02T22:44:10.763186image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-02T22:44:11.022749image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:11.148761image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-02T22:44:11.421043image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:11.546580image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:11.682920image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-10-02T22:44:21.039777image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-02T22:44:21.227550image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-02T22:44:21.406341image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-02T22:44:21.592818image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-02T22:44:21.768296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-02T22:44:12.074678image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:12.638703image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:13.261777image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-02T22:44:13.558382image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

CoM IDCommon NameScientific NameGenusFamilyDiameter Breast HeightYear PlantedDate PlantedAge DescriptionUseful Life ExpectencyUseful Life Expectency ValuePrecinctLocated inUploadDateCoordinateLocationLatitudeLongitudeEastingNorthing
01492924Flame TreeBrachychiton acerifoliusBrachychitonMalvaceaeNaN2011.012/08/2011NaNNaNNaNNaNPark01/10/2020(-37.795282936672386, 144.9173922130217)-37.795283144.917392316635.335814855.56
11019881Golden HoneylocustGleditsia triacanthosGleditsiaFabaceae16.01900.002/01/2000Semi-Mature31-60 years60.0NaNStreet01/10/2020(-37.79566841931897, 144.95597171021834)-37.795668144.955972320033.395814887.79
21031022Spotted GumCorymbia maculataCorymbiaMyrtaceae31.01997.012/12/1997Semi-Mature31-60 years60.0NaNStreet01/10/2020(-37.8210392069869, 144.91843779301422)-37.821039144.918438316791.085811999.52
31050432Black WattleAcacia mearnsiiAcaciaFabaceaeNaN1998.018/12/1998NaNNaNNaNNaNPark01/10/2020(-37.78286326814045, 144.95442254132493)-37.782863144.954423319865.885816305.74
41029800Variegated ElmUlmus minorUlmusUlmaceae22.01997.012/08/1997Semi-Mature11-20 years20.0NaNStreet01/10/2020(-37.822346761279306, 144.96499971222198)-37.822347144.965000320892.835811944.75
51286590London PlanePlatanus x acerifoliaPlatanusPlatanaceae59.0NaNNaNMature1-5 years (>50% canopy)5.0NaNStreet01/10/2020(-37.789091353254534, 144.92975715633244)-37.789091144.929757317708.915815566.81
61781809WattleAcacia unknownAcaciaFabaceaeNaN2020.019/03/2020NaNNaNNaNNaNPark01/10/2020(-37.820435683194596, 144.94359660008945)-37.820436144.943597319004.205812115.55
71048800Lemon BottlebrushCallistemon pallidusCallistemonMyrtaceaeNaN1998.015/12/1998NaNNaNNaNNaNPark01/10/2020(-37.78971502333605, 144.94675314698821)-37.789715144.946753319207.145815530.62
81525508Norfolk Island PineAraucaria heterophyllaAraucariaAraucariaceaeNaN2012.027/04/2012NaNNaNNaNNaNStreet01/10/2020(-37.81398704347387, 144.944689194818)-37.813987144.944689319084.635812833.25
91784818Red PokersHakea bucculentaHakeaProteaceaeNaN2020.023/06/2020NaNNaNNaNNaNPark01/10/2020(-37.82681988077671, 144.98375951144155)-37.826820144.983760322554.835811484.20

Last rows

CoM IDCommon NameScientific NameGenusFamilyDiameter Breast HeightYear PlantedDate PlantedAge DescriptionUseful Life ExpectencyUseful Life Expectency ValuePrecinctLocated inUploadDateCoordinateLocationLatitudeLongitudeEastingNorthing
746801034163Huntingdon ElmUlmus x hollandicaUlmusUlmaceae66.01998.024/09/1998Mature<1 year1.0NaNPark01/10/2020(-37.82700832792828, 144.98381277992095)-37.827008144.983813322559.975811463.39
746811036571English ElmUlmus proceraUlmusUlmaceae110.01998.027/10/1998Mature11-20 years20.0NaNPark01/10/2020(-37.80134922468855, 144.9730968954069)-37.801349144.973097321555.015814290.26
746821046335River red gumEucalyptus camaldulensisEucalyptusMyrtaceaeNaN1998.012/07/1998NaNNaNNaNNaNPark01/10/2020(-37.788357846785786, 144.95262311600283)-37.788358144.952623319720.765815692.56
746831604096Chinese ElmUlmus parvifoliaUlmusUlmaceaeNaN2015.014/08/2015NaNNaNNaNNaNStreet01/10/2020(-37.820559637253, 144.9445744716103)-37.820560144.944574319090.585812103.69
746841440058English OakQuercus roburQuercusFagaceaeNaN2009.012/10/2009NaNNaNNaNNaNPark01/10/2020(-37.8310841406414, 144.97480645272424)-37.831084144.974806321777.075810993.96
746851015316London PlanePlatanus x acerifoliaPlatanusPlatanaceae36.01900.002/01/2000Semi-Mature11-20 years20.0NaNStreet01/10/2020(-37.797173995067354, 144.95074876642542)-37.797174144.950749319577.155814710.65
746861656789Turkey OakQuercus cerrisQuercusFagaceaeNaN2017.026/04/2017NaNNaNNaNNaNStreet01/10/2020(-37.8010568898896, 144.96525358940465)-37.801057144.965254320863.725814307.69
746871635035Prickly-leaved paperbarkMelaleuca nodosaMelaleucaMyrtaceaeNaN2016.008/06/2016NaNNaNNaNNaNPark01/10/2020(-37.82092659909152, 144.94676981194382)-37.820927144.946770319284.725812067.22
746881584555Swamp SheoakCasuarina obesaCasuarinaCasuarinaceaeNaN2015.018/06/2015NaNNaNNaNNaNPark01/10/2020(-37.79512590199677, 144.95222072733162)-37.795126144.952221319701.785814940.76
746891025972English ElmUlmus proceraUlmusUlmaceae116.01997.024/11/1997Mature11-20 years20.0NaNPark01/10/2020(-37.80981079456008, 144.9899121616535)-37.809811144.989912323055.755813383.30